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        <title>Document Embedding Enhanced Event Detection with Hierarchical and Supervised Attention</title>
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        <namePart type="given">Yue</namePart>
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    <name type="personal">
        <namePart type="given">Xiaolong</namePart>
        <namePart type="family">Jin</namePart>
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    <name type="personal">
        <namePart type="given">Yuanzhuo</namePart>
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    <name type="personal">
        <namePart type="given">Xueqi</namePart>
        <namePart type="family">Cheng</namePart>
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    <abstract>Document-level information is very important for event detection even at sentence level. In this paper, we propose a novel Document Embedding Enhanced Bi-RNN model, called DEEB-RNN, to detect events in sentences. This model first learns event detection oriented embeddings of documents through a hierarchical and supervised attention based RNN, which pays word-level attention to event triggers and sentence-level attention to those sentences containing events. It then uses the learned document embedding to enhance another bidirectional RNN model to identify event triggers and their types in sentences. Through experiments on the ACE-2005 dataset, we demonstrate the effectiveness and merits of the proposed DEEB-RNN model via comparison with state-of-the-art methods.</abstract>
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%0 Conference Proceedings
%T Document Embedding Enhanced Event Detection with Hierarchical and Supervised Attention
%A Zhao, Yue
%A Jin, Xiaolong
%A Wang, Yuanzhuo
%A Cheng, Xueqi
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 jul
%I Association for Computational Linguistics
%C Melbourne, Australia
%F zhao-etal-2018-document
%X Document-level information is very important for event detection even at sentence level. In this paper, we propose a novel Document Embedding Enhanced Bi-RNN model, called DEEB-RNN, to detect events in sentences. This model first learns event detection oriented embeddings of documents through a hierarchical and supervised attention based RNN, which pays word-level attention to event triggers and sentence-level attention to those sentences containing events. It then uses the learned document embedding to enhance another bidirectional RNN model to identify event triggers and their types in sentences. Through experiments on the ACE-2005 dataset, we demonstrate the effectiveness and merits of the proposed DEEB-RNN model via comparison with state-of-the-art methods.
%R 10.18653/v1/P18-2066
%U https://aclanthology.org/P18-2066
%U https://doi.org/10.18653/v1/P18-2066
%P 414-419

Markdown (Informal)

[Document Embedding Enhanced Event Detection with Hierarchical and Supervised Attention](https://aclanthology.org/P18-2066) (Zhao et al., ACL 2018)

• Document Embedding Enhanced Event Detection with Hierarchical and Supervised Attention (Zhao et al., ACL 2018)

ACL

• Yue Zhao, Xiaolong Jin, Yuanzhuo Wang, and Xueqi Cheng. 2018. Document Embedding Enhanced Event Detection with Hierarchical and Supervised Attention. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 414–419, Melbourne, Australia. Association for Computational Linguistics.